SelfDecode.com, Miami, Florida, United States of America.
PLoS One. 2022 May 9;17(5):e0259327. doi: 10.1371/journal.pone.0259327. eCollection 2022.
The vast majority of human traits, including many disease phenotypes, are affected by alleles at numerous genomic loci. With a continually increasing set of variants with published clinical disease or biomarker associations, an easy-to-use tool for non-programmers to rapidly screen VCF files for risk alleles is needed. We have developed EZTraits as a tool to quickly evaluate genotype data against a set of rules defined by the user. These rules can be defined directly in the scripting language Lua, for genotype calls using variant ID (RS number) or chromosomal position. Alternatively, EZTraits can parse simple and intuitive text including concepts like 'any' or 'all'. Thus, EZTraits is designed to support rapid genetic analysis and hypothesis-testing by researchers, regardless of programming experience or technical background. The software is implemented in C++ and compiles and runs on Linux and MacOS. The source code is available under the MIT license from https://github.com/selfdecode/rd-eztraits.
绝大多数人类特征,包括许多疾病表型,都受到众多基因组位置的等位基因影响。随着越来越多具有已发表临床疾病或生物标志物关联的变异体的出现,非程序员需要一种易于使用的工具来快速筛选 VCF 文件中的风险等位基因。我们开发了 EZTraits 作为一种工具,用于根据用户定义的一组规则快速评估基因型数据。这些规则可以直接在脚本语言 Lua 中定义,用于使用变体 ID(RS 编号)或染色体位置的基因型调用。或者,EZTraits 可以解析简单直观的文本,包括“任意”或“全部”等概念。因此,EZTraits 旨在支持研究人员进行快速遗传分析和假设检验,无论其编程经验或技术背景如何。该软件是用 C++编写的,可在 Linux 和 MacOS 上编译和运行。源代码可在 MIT 许可证下从 https://github.com/selfdecode/rd-eztraits 获得。